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International Journal of Latest Research in Science and Technology

DOI:10.29111/ijlrst   ISRA Impact Factor:3.35

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MACHINE LEARNING METHODS FOR THE PSYCHOLOGICAL DISTRESS (OR DEPRESSIVE AND ANXIETY SYMPTOMS) OF THE GREEK GENERAL POPULATION DURING THE COVID19 LOCKDOWN

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International Journal of Latest Research in Science and Technology Vol.10 Issue 2, pp 37-41,Year 2021

MACHINE LEARNING METHODS FOR THE PSYCHOLOGICAL DISTRESS (OR DEPRESSIVE AND ANXIETY SYMPTOMS) OF THE GREEK GENERAL POPULATION DURING THE COVID19 LOCKDOWN

Georgia Konstantopoulou, Theodoros Iliou, Katerina Karaivazoglou, Konstantinos Assimakopoulos, Panagiotis Alexopoulos and George Anastassopoulos

Received : 27 August 2021; Accepted : 10 September 2021 ; Published : 30 September 2021

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Abstract

The third wave of the pandemic due to COVID-19 promotes fear on a social level, but also on an individual level exacerbates anxiety and symptoms that look like depression and seems to lead to other mental issues (eg mood problems, sleep problems, phobia-like behaviors, panic-like symptoms). While most of us at this time may experience anxiety and "heavy" mood due to incarceration, in a significant portion of the world the condition has triggered serious psychological problems, with experts worried that these growing disorders will continue to exist and after the end of the pandemic. Research shows that machine learning techniques help significantly in tool development by helping physicians anticipate mental disorders and support patient care. Early detection and treatment can help any patient in the early stages of any disease. In this work we carried out stress and depression prediction using seven Machine Learning algorithms achieving 100% correct prediction with Multilayer Perceptron classifier.

Key Words   
Machine learning, COVID-19; psychological distress; anxiety; depression, lock-down, Hospital anxiety
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References
  1. World Health Organization (WHO). WHO Official Updates – Coronavirus Disease, 2019. WHO, 2020 (www.who.int/emergencies/diseases/novel-coronavirus-2019).
  2. Luo M, Guo L, Yu M, Jiang W, Wang H. The psychological and mental impact of coronavirus disease 2019 (COVID-19) on medical staff and general public – a systematic review and meta-analysis. Psychiatry Res 2020; 291: 113190.
  3. Serafini G, Parmigiani B, Amerio A, Aguglia A, Sher L, Amore M. The psychological impact of COVID-19 on the mental health in the general population. QJM 2020; 113(8): 531–7.
  4. Wang C, Pan R, Wan X, Tan Y, Xu L, Ho CS, et al. Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in China. Int J Environ Res Public Health 2020; 17(5): 1729.
  5. Varshney M, Parel JT, Raizada N, Sarin SK. Initial psychological impact of COVID-19 and its correlates in Indian community: an online (FEEL-COVID) survey. PLoS One 2020; 15(5): e0233874.
  6. Rodríguez-Rey R, Garrido-Hernansaiz H, Collado S. Psychological impact of COVID-19 in Spain: early data report. Psychol Trauma 2020; 12(5): 550–2.
  7. Le Shi et al, (2020) «Prevalence of and Risk Factors Associated With Mental Health Symptoms Among the General Population in China During the Coronavirus Disease 2019 Pandemic» JAMA Network Open. 2020;3(7):e2014053. doi:10.1001/jamanetworkopen.2020.14053
  8. CDC 2021 Mental Health and Coping during COVID-19, Accessed 11/4/2021. https://www.cdc.gov/coronavirus/2019-ncov/daily-life-coping/managing-stress-anxiety.html
  9. Katerina Karaivazoglou, Georgia Konstantopoulou, Maria Kalogeropoulou, Theodoros Iliou, Theofanis Vorvolakos, Konstantinos Assimakopoulos, Philippos Gourzis and Panagiotis Alexopoulos (2021) BJPsych Open 7, e59, 1–7. doi: 10.1192/bjo.2021.17
  10. Pintelas et al, 2018 « A review of machine learning prediction methods for anxiety Disorders» DSAI 2018, June 20–22, 2018, Thessaloniki, Greece
  11. Iliou T., Konstantopoulou G., Stephanakis I., Anastasopoulos K., Lymberopoulos D., Anastassopoulos G. (2018) Iliou Machine Learning Data Preprocessing Method for Stress Level Prediction. In: Iliadis L., Maglogiannis I., Plagianakos V. (eds) Artificial Intelligence Applications and Innovations. AIAI 2018. IFIP Advances in Information and Communication Technology, vol 519. Springer, Cham, https://doi.org/10.1007/978-3-319-92007-8_30.
  12. Iliou et al 2019 «ILIOU Machine Learning Preprocessing Method for Depression Type Prediction» Evolving Systems (2019) 10:29–39 DOI 10.1007/s12530-017-9205-9
  13. Konstantopoulou et al, 2020 “Detection of post-traumatic stress disorder (PTSD) symptoms associated with coronavirus disease 2019 (covid-19) in the student population”, European Journal of Special Education Research, ISSN: 2501-2428, 2020
  14. Jon D Elhai et al, 2021 “ Modeling anxiety and fear of COVID-19 using machine learning in a sample of Chinese adults: associations with psychopathology, sociodemographic, and exposure variables” Mar;34(2):130-144.doi: 10.1080/10615806.2021.1878158.
  15. Stephanie Josephine Eder, et al, 2021 “Predicting fear and perceived health during the COVID-19 pandemic using machine learning: A cross-national longitudinal study” https://doi.org/10.1371/journal.pone.0247997
  16. Tracy A Prout et al 2020, “Identifying Predictors of Psychological Distress During COVID-19: A Machine Learning Approach”, https://doi.org/10.3389/fpsyg.2020.586202
  17. Zigmond AS, Snaith RP. The Hospital Anxiety and Depression Scale. Acta Psychiatr Scand 1983; 67(6): 361–70.
  18. Michopoulos I, Douzenis A, Kalkavoura C, Christodoulou C, Michalopoulou P, Kalemi G, et al. Hospital Anxiety and Depression Scale (HADS): validation in a Greek general hospital sample. Ann Gen Psychiatry 2008; 7: 4.
  19. R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection". Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, vol. 2, no. 12, 1995, pp. 1137–1143.
  20. Waikato Environment for Knowledge Analysis, Data Mining Software in Java, available online: http://www.cs.waikato.ac.nz/ml/index.html, Accessed 19/5/2021.
  21. Matthews, B. W. (1975). "Comparison of the predicted and observed secondary structure of T4 phage lysozyme". Biochimica et Biophysica Acta (BBA) - Protein Structure. 405 (2): 442–451. doi:10.1016/0005-2795(75)90109-9.
  22. Lehmann, E. L.; Casella, George (1998). Theory of Point Estimation (2nd ed.). New York: Springer. ISBN 0-387-98502-6. MR 1639875.
  23. Powers, David M W (2011). "Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation". Journal of Machine Learning Technologies.
    24. Adrian B. R. Shatte et all (2019) “Machine learning in mental health: A systematic scoping review of methods and applications” Psychological Medicine 49(9):1-23
To cite this article

Georgia Konstantopoulou, Theodoros Iliou, Katerina Karaivazoglou, Konstantinos Assimakopoulos, Panagiotis Alexopoulos and George Anastassopoulos , " Machine Learning Methods For The Psychological Distress (or Depressive And Anxiety Symptoms) Of The Greek General Population During The Covid19 Lockdown ", International Journal of Latest Research in Science and Technology . Vol. 10, Issue 2, pp 37-41 , 2021

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